Earth observation imagery data has been collected and analysed for decades and is an essential part of many natural resource management, geological and mineral exploration, agricultural management, environmental monitoring and aerial mapping systems, to name just a few applications. Whether the data is obtained from satellite, airborne sensing or other systems, and whether it consists of all or a combination of photogrammetry, hyperspectral, multitemporal, optical, thermal and microwave (and other similar systems) data, the problem with analysis is much the same: large volumes of data must be processed quickly and accurately, without distortion.
For example, remote sensing has been a central tool for environmental management and monitoring at local, regional and global scales. The need to monitor the habitat of endangered species, predict flood patterns, and evaluate the health of coral reef environments, has never been more acute. To address the increasingly complex web of influences on our ecosystems, today's environmental stakeholders are demanding current information, new analysis techniques and support for new sensors. These systems often need to integrate datasets from a variety of sources and apply best-practice analytical approaches, including for example: data fusion, spectral and spatial analysis, classification, thematic mapping, and integration with Geographic Information Systems (GIS).
There is therefore a need for an improved method of and system for processing large volumes of Earth observation imagery data with efficiency, accuracy, and the ability to integrate several different data systems together.